A genome-wide CRISPR screen maps endogenous regulators of PPARG gene expression in bladder cancer

Summary Peroxisome proliferator-activated receptor gamma (PPARγ) is a nuclear receptor central in the regulation of key cellular processes including cell metabolism, tissue differentiation, and regulation of the immune system. PPARγ is required for normal differentiation of the urothelium and is thought to be an essential driver of the luminal subtype of bladder cancer. However, the molecular components that regulate PPARG gene expression in bladder cancer remain unclear. Here, we developed an endogenous PPARG reporter system in luminal bladder cancer cells and performed genome-wide CRISPR knockout screening to identify bona fide regulators of PPARG gene expression. Functional validation of the dataset confirmed GATA3, SPT6, and the cohesin complex components SMC1A, and RAD21, as permissive upstream positive regulators of PPARG gene expression in luminal bladder cancer. In summary, this work provides a resource and biological insights to aid our understanding of PPARG regulation in bladder cancer.


INTRODUCTION
and progression, and thus, unraveling the regulatory mechanisms that control PPARG gene expression in bladder cancer has important implications for the understanding and treatment of the disease. Furthermore, as PPARg is a regulator of normal urothelial differentiation, 29,30 knowledge of its regulation is important for understanding the fundamental biology of bladder development and function. We performed a genome-wide CRISPR screen to identity endogenous regulators of PPARG gene expression in bladder cancer. Our data provide insight into the molecular mechanisms regulating PPARG gene expression and identify potential therapeutic targets in this pathway.

RESULTS
Design and validation of a genetic reporter system to monitor endogenous PPARG gene expression In order to study the regulation of PPARG gene expression in bladder cancer, we developed a cell-based reporter system that reflected endogenous changes in PPARG expression. Since high PPARG expression is a feature of luminal MIBC, we sought to identify a luminal bladder cancer cell line with high endogenous levels of PPARG to use for the assay. Using previously published RNA sequencing (RNAseq) data and molecular subtyping calls, 17 luminal bladder cancer cell lines were identified. 18 Consistent with previous reports, many luminal cell lines were enriched for PPARG mRNA ( Figure S1A). UM-UC1, UM-UC9, UM-UC14, and RT112 cells also had high PPARg expression at the protein level, with UM-UC9 having the highest expression, consistent with a known amplification of the PPARG gene ( Figure S1B). 16,31 Based on these data, and the consistency in subtyping of RT112 cells as luminal by multiple groups and methods, 18,32,33 RT112 cells were selected to develop our reporter system. To generate a reporter system able to monitor variations in endogenous PPARG gene expression, enhanced green fluorescence protein (eGFP) and neomycin resistance genes were inserted immediately upstream of, and in frame with, the PPARG coding sequence in RT112 cells ( Figure 1A). Given the size of eGFP and its propensity for dimerization, 34 a fusion of eGFP to PPARg would likely affect the functionality of PPARg. Therefore, 2A sequences, encoding self-cleaving peptides, were introduced following the eGFP and neomycin resistance genes. This created a fusion gene under the transcriptional control of the endogenous PPARG regulatory system, without affecting the downstream function of PPARg.
To create a double-strand DNA cut at the PPARG 5 0 region, a plasmid encoding Cas9 and a targeting CRISPR guide RNA was transiently co-transfected with the donor plasmid (PPARG HR -eGFP, Figure 1A) into RT112 cells. The reporter sequence was positioned in between two 1 kb DNA sequences (left arm (LA), right arm (RA)) homologous to the regions surrounding the CRISPR cut site. This provided the cells with the appropriate substrate to fix the CRISPR-induced damage using their natural homology-directed repair (HDR) system, thus resulting in the introduction of the reporter gene at the desired locus. To increase the specificity of the HDR process, a diphtheria toxin A (DTA) cassette was added downstream of the RA sequence in the PPARG HR -eGFP plasmid, in order to select against random integration of the vector in non-specific regions of the genome. 35 Moreover, to prevent DTA toxicity by its expression from the donor plasmid, and to increase the recombination efficiency, 36 PPARG HR -eGFP was linearized prior to transfection. Following transfections, cells carrying the reporter construct were selected by florescence-activated cell sorting to generate a pure population of reporter cells (RT112-PPARg GFP ) ( Figure 1B).
We next sought to validate the reporter system. First, the correct insertion of the reporter gene was confirmed by PCR amplification of sequences spanning the genomic DNA and inserted donor template ( Figure S2). Next, we knocked down PPARG with short interfering RNA (siRNA) and evaluated changes in eGFP expression. Knockdown (KD) of PPARG in RT112-PPARg GFP cells led to a 76% and 87% decrease in GFP and PPARG mRNA levels, respectively, compared to scrambled siRNA controls (siCtrl) ( Figure 1C). This was consistent with an 81% decrease in GFP fluorescence and a 93% decrease in PPARg protein ( Figures 1D-1F). We then questioned whether insertion of the reporter construct affected baseline expression of PPARg. PPARg mRNA and protein levels were assessed in wild-type RT112 (WT) and RT112-PPARg GFP cells by RT-qPCR and Western blot. RT112-PPARg GFP cells had a 30% decrease in PPARG mRNA, and a 20% in PPARg protein compared with WT cells (Figures 1E and 1F). Finally, no differences were observed in the magnitude of the PPARG KD between WT and RT112-PPARg GFP cells at either an mRNA (both 88%) or protein (90% and 93%, respectively) level ( Figures 1C and 1F Figure 2. Performing the genome-wide knockout screen and ranking the hits (A) Diagram illustrating the steps of the screen. RT112-PPARg GFP cells were transduced with a CRISPR lentiviral library and transduced cells were enriched by antibiotic selection. Knockout of genes affecting PPARG expression resulted in altered GFP production. Live (propidium iodide negative) cells were sorted by fluorescence activated cell sorting (FACS) according to their GFP fluorescence intensity (GFP lo and GFP hi by approximate quartiles, and total GFP + (GFP tot )), and sgRNA enrichment was evaluated by next-generation sequencing (NGS). iScience Article cultured under antibiotic selection for a period of 7 days to select against un-transduced cells ( Figure S3A), to allow for genome editing to take place, and for the screened phenotypes to become detectable. Cells were then collected and live (propidium iodide negative) GFP hi (upper quartile) and GFP lo (lower quartile) cells were sorted by fluorescence-activated cell sorting. Total live GFP + cells (GFP tot ) were also sorted as a control group ( Figure S3B). Deletion of factors involved in the upregulation of PPARG transcription should result in lower GFP expression, thus sgRNAs targeting those factors would be enriched in the GFP lo group and depleted from the GFP hi , compared to the GFP tot control, and vice versa.
Genomic DNA was extracted from the three populations (GFP lo , GFP hi , and GFP tot ) and the abundance of each sgRNA was quantified by next-generation sequencing. The computational analysis of the CRISPR screen included guide counting and statistical tests for the assessment of guide and gene level significance, as well as quality control for sequencing, library representation, and the reproducibility of results from biological experimental replicates. Analysis of the sgRNA abundance for the GFP tot control samples confirmed the uniform representation of sgRNAs in both screens with almost no difference between experimental replicates (AUC = 0.67075 and 0.67256) ( Figure S4A). In addition, no significant differences were found between guide counts in the GFP tot samples between screen 1 and 2, and only very small differences were observed between replicates for the GFP lo and GFP hi samples ( Figures S4B and S4C).
Given the equivalence in sgRNA distribution in the GFP tot samples, data from the two independent replicates were combined to improve power and confidence in hit calling. The 19,114 genes targeted by the CRISPR library were then ranked using the Model-based Analysis of Genome-wide CRISPR/Cas9 Knockout (MAGeCK) algorithm, 37 thus obtaining a list of genes, ranked for their enrichment or depletion in each sample. Comparing different lists, genes with equivalent rank appear closer to a line of slope 1 (line of rank congruency). By comparing these ranks within each sample, we identified a subset of genes, which had statistically significant scores in both the lists for positive and negative enrichment ( Figures 2B and  2C). A similar cluster was identified by an equivalent analysis comparing GFP hi to GFP lo cells ( Figures 2D  and 2E). This cross-list comparison identified a group of hits, including MYC, EIF2AK4, ACTL6B, PELO, UFSP2, BAX, and CKS1B, which were always found to be in the top 20 of any MAGeCK rank. Therefore, an additional heuristic filter was applied to filter out these potentially false positive hits. In each experimental group, enriched gene knockouts were organized according to an adjusted order, calculated as the difference between the MAGeCK-derived negative and positive ranks. This adjusted rank was used for our analyses, and, despite modifying the original MAGeCK ranking, was found to preserve the major order of the remaining potential true hits ( Figures 2F and 2G).
An internal validation for the reliability of our screen came from the comparison of the adjusted hit ranks between the GFP lo and GFP hi cells. Top hits enriched in the positive rank from the GFP lo group were also highly positioned in the negative rank from the GFP hi sample ( Figure 2H). This means that sgRNAs enriched in GFP lo were depleted from GFP hi sample and vice versa. This relationship persisted to a lesser degree when positive GFP hi and negative GFP lo ranks were compared ( Figure 2I). FDR and p-value analyses showed that, despite remaining highly significant, there was a drop in significance between hits placed in the 20 th (CTCF) and 21 st (MED13) positions in the GFP lo group ( Figures 2J and 2K). This rank is also approximately where the hits started diverging from the line of rank congruency between the positive GFP lo and negative GFP hi rank ( Figure 2H). A similar drop was observed at around position 35 for the GFP hi counterpart of the screen ( Figures 2L and 2M).
Lists of the top candidate positive and negative regulators (herein referred to as ''top hits'') were then generated. Genes ranked within the top 50 enriched genes and with an FDR <0.01, for the GFP lo (positive regulators) and GFP hi (negative regulators) samples were included in these lists. 47 and 50 hits from the GFP lo and GFP hi samples, respectively, fit these criteria (Tables 1 and S1). Taken together, these analyses support the validity of the screen, and provided a list of potential regulators of PPARG in bladder cancer.

Identified PPARG regulators are linked to MIBC
In order to investigate potential regulators of PPARG expression with relevance in MIBC, we correlated the expression of the top hits with PPARG in a cohort of luminal (LumP, LumNS, and LumU) and basal (Ba/Sq) tumors from the TCGA database that lacked PPARG copy number amplification (n = 303  Figure 3C). A similar analysis looking at negative regulators of PPARG expression revealed 33 hits that significantly (adj. p < 0.05) correlated with PPARG ( Figure S5A). Of the hits, 24 were negatively correlated with PPARG, and 25 were enriched in basal tumors compared with luminal in MIBC ( Figure S5B).
We next examined the putative target gene sets (regulons) 18 of three main drivers of the luminal biology in MIBC (PPARG, GATA3, and FOXA1) and found nine of the hits among the target genes ( Figure 3D). Interestingly, PPARg and GATA3 are present in each other's regulon, suggesting the possibility of a positive feedback loop for the two transcription factors. Furthermore, many of the hits identified as positive regulators of PPARG gene expression by our screen fall within the regulons of GATA3 and FOXA1, suggesting a potential indirect role for these transcription factors, in particular FOXA1, whose putative regulon does not include PPARG ( Figure 3D).
The Cistrome database provides information regarding the potential of a protein to affect the expression of a specific gene of interest. 38 This probability is given through a regulatory potential score (RPS) in which chromatin immunoprecipitation sequencing (ChIP-seq) data of transcription factors or chromatin regulators are integrated with differential gene expression analyses through the BETA algorithm to infer direct target genes. 39 Of the positive hits, eight appeared among the potential regulatory factors of PPARG expression with a positive RPS score ( Figure 3E). Here again, GATA3 was among the most promising candidates.

Distinct cellular processes contribute to the regulation of PPARG gene expression
We next sought to gain insight into the biological mechanisms that regulate PPARG gene expression as identified by the screen. To do this, enrichment analysis was performed using the g:GOSt tool provided by the g:Profiler platform that utilizes Gene Ontology (GO) terms and the biological pathway database, Reactome (REAC), to identify the biological pathways enriched in the list of putative positive regulators ( Figures 4A and 4B). This analysis showed that most of the top positive hits were localized within the nucleus and had transcriptional regulatory activity. Moreover, many were involved in processes known to be regulated by PPARg, such as macromolecule biosynthesis and cellular differentiation programs. 15 Similar analysis of the top 50 negative regulator hits suggested that they were mainly localized in the nucleus, and were involved in activities related to cell cycle regulation ( Figures S6A and S6B).
Finally, we aimed to identify functional groups that could reveal potential interdependencies between our hits. To do this, we utilized STRING, a biological database of known and predicted protein-protein interactions, and identified five major functional groups within the top positive regulator hits ( Figure 4C). The groups included a cluster of transcription factors important in the biology of luminal bladder cancer (GATA3 and FOXA1), elements of the cohesin and mediator complexes involved in regulation of transcription and chromatin structure maintenance, the aryl hydrocarbon receptor involved in the detoxification from xenobiotic chemicals, and the KEAP1/CUL3 E3-ubiquitin ligase complex, a key player in the oxidative stress response. Similar analysis of the top negative hits identified a major cluster of 18 proteins involved in cell replication, and DNA synthesis and maintenance systems. Other smaller subgroups involved in RNA processing, protein folding, and general transcription factor scaffolding components were also identified ( Figure S6B). Together, these data suggest a possible mechanism of PPARG repression related to cell cycle progression.  iScience Article PPARG gene expression is regulated by GATA3 in luminal bladder cancer We next sought to validate a subset of the hits identified in the knockout screen. The targets were chosen among the top-ranking hits focusing on those at the core of the most relevant identified functional clusters. As a first proof-of-principle, the selected candidate genes were knocked down in RT112-PPARg GFP cells by siRNA and changes in eGFP expression were assessed by flow cytometry. Knocking down GATA3, SMC1A, RAD21, SUPT6H, MED12, and ARNT led to a consistent decreased in eGFP expression compared to control siRNA ( Figure 5A).
Of these hits, GATA3 is also associated with luminal MIBC 23 and had the strongest correlation with PPARG expression in patient samples ( Figures 3A-3C). To validate the effect of GATA3 perturbation on PPARG gene expression, GATA3 was knocked down in RT112 and UM-UC1 cells, which led to a 38%-52% decrease of PPARG mRNA, and a 10%-70% decrease in PPARg protein by Western blot (Figures 5B-5D). Moreover, this also resulted in a similar decrease in the expression of the PPARg target gene PSCA in both cell lines ( Figures 5C and 5D). These data indicate that PPARG gene expression is affected by GATA3 in luminal bladder cancer.

The cohesin complex contributes to the regulation of PPARG gene expression in luminal bladder cancer
Several of the putative positive regulators of PPARG expression are known to participate in chromatin remodeling and maintenance functions. Many of these (e.g. SMC1A and RAD21) are direct components of the cohesin complex while others, such as SPT6 (encoded by SUPT6H), are functionally related. 40,41 SMC1A, RAD21, and SUPT6H were each silenced by siRNA in RT112 cells, and PPARG mRNA was quantified by RT-qPCR. KD of SMC1A, RAD21, and SUPT6H each resulted in a significant (35%-58%) decrease in PPARG gene expression ( Figures 6A-6C). Consistent with this, siRNA targeting RAD21 and SUPT6H in RT112 and UM-UC1 cells led to a reduction in PPARg protein compared to the scrambled siRNA control (Figures 6D and 6E). Despite a similar reduction of PPARG mRNA expression following KD of each of iScience Article the three genes, only loss of RAD21 and SUPT6H, but not SMC1A, led to a consistent robust reduction in PPARg protein. In addition, KD of SMC1A also led to a decrease in RAD21 protein, suggesting a feedback loop between these two proteins, and that SMC1A is required for RAD21-dependent reduction in PPARg ( Figures 6D and 6E). In summary, these data suggest that the cohesin complex promotes expression of PPARG in luminal bladder.

DISCUSSION
Finding a clear and reliable phenotype is the first major step in large-scale gene expression screens. Historically, indirect reporter systems have been used, in which reporter genes, such as luciferase, eGFP, b-galactosidase, or alkaline phosphatase, are linked to a putative promoter for the gene in study. 42,43 Despite the fact that this approach is still widely used, it carries intrinsic drawbacks that decrease the biological relevance of its findings. By relying on the random insertion of the reporter cassette in the genome, common reporter systems can result in the unwanted disruption of genes potentially relevant for the phenotype in analysis. Another risk is the possible insertion of the reporter in silent or highly transcribed genomic regions that can confound the readout. Moreover, this technique does not consider the full complexity of gene regulation, which is fine-tuned by a combination of promoter sequences, adjacent DNA regions, enhancers, epigenetics, and other highly context-dependent regulatory mechanisms. [44][45][46][47] Finally, a specific promoter sequence needs to be verified a priori, and this is frequently assessed in iScience Article experiments carried out in different biological systems. Thus, the usage of an exogenous and simplistic promoter-reporter system can lead to generation of misleading and non-biologically relevant data.
CRISPR knockin technology is a powerful tool to deliver an exogenous reporter in a specific site within the genome, allowing for expression of the reporter under the same regulation as the gene of interest without the requirement for previous promoter knowledge or other assumptions. This can then be combined with CRISPR knockout technology to perform a highly specific and large-scale genome-wide screen 48-50 . Given the complexity of functions and regulatory mechanisms of PPARg, which are highly variable within distinct biological contexts, it was imperative to use a reporter system knocked into the endogenous locus of a cell type relevant to our disease of interest. We therefore utilized CRISPR knockin technology to create a transgenic luminal bladder cancer cell line engineered to express GFP and PPARG proportionally, so that fluorescence could be used as a readout to identify endogenous regulators of PPARG expression. With this reporter line, we performed a high-throughput genome-wide CRISPR knockout screen using the Brunello lentiviral library which comprises 76,441 unique sgRNAs targeting 19,114 coding genes and an additional 1000 non-targeting sgRNAs for control. Compared to previously published sgRNA collections, the Brunello library was designed with optimized rules to improve on-target activity and lower off-target effects. 51 Together, these systems provided a robust method of identifying biologically and clinically relevant regulators of PPARG gene expression.
In the GFP lo group, we found potential positive regulators of PPARG expression which correlated with PPARG mRNA level and were enriched in the luminal subtype. This, combined with the identification of functional clusters within our list of hits, highlighted the efficacy of the screen. Correlation of our hits with PPARG in MIBC samples was used as a first means to filter our screen results to include those with likely relevance to bladder cancer in vivo. However, this may have falsely excluded hits with biological significance as a result of potential adverse effects for the overall tumor survival in vivo. Furthermore, the presence of mixed populations of cells in the whole-tissue RNAseq data could confound the analysis since different cell types express different amounts of each of the genes (including PPARG). Consistent with this, although expression of SMC1A and RAD21 both negatively correlated with PPARG in MIBC samples, they were validated as positive regulators of PPARG gene expression in two independent cell lines. Therefore, further analysis and validation of the hits identified in our screen has the potential to generate significantly more biological insight into regulators of PPARG expression specifically in bladder cancer cells.
We choose to focus our validation on positive regulators of PPARG as they had the most obvious potential relevance for luminal MIBC. By using a luminal bladder cancer cell line with high PPARG expression, we likely selected a system that lacked activity of many of the regulatory mechanisms that actively suppress PPARG gene expression, therefore limiting our ability to identify these mechanisms using a gene knockout system. Furthermore, pathway enrichment analysis suggested the involvement of the top hits in broad cellular processes including cell cycle progression, RNA splicing, and protein translation. Although these mechanisms could potentially alter PPARg expression, they could also be an artifact of the screen by reducing cell duplication and protein turnover, leading to an accumulation of eGFP within the cells. Nonetheless, within our top negative hits are some that have been previously identified as important in basal bladder cancer, including CDK6 and p63 (encoded by TP63). 52 p63 is a known driver of the basal subtype and it inhibits genes associated with epithelial differentiation in normal human urothelial cells, including PPARG. 17,53,54 Although these findings warrant further investigation, an analogous screen performed on a basal cell line expressing low PPARg level could support identification of relevant inhibitors of PPARg expression. Alternatively, a CRISPR-based gene activator screen could be performed using the RT112-eGFP reporter cell line. Future studies that combine whole-genome activator and knockout screens in both luminal and basal bladder cancer will provide a comprehensive framework of the various positive and negative regulatory mechanisms that are necessary and/or sufficient to modulate PPARG gene expression in MIBC.
GATA3 is a member of the GATA-binding protein family of transcription factors that recognize the DNA consensus sequence A/T-GATA-A/G. 55 GATA3 has been extensively studied for its role in hematopoietic tissues and, in particular, for its central role in the development in multiple immune cell lineages. 56,57 It has also been implicated in the proper development of numerous tissues including skin, kidney, rectum, breast, and bladder. 58 In the ductal epithelium of the mammary gland, GATA3 is highly expressed in the luminal cells while it is absent from the basal layer containing a pool of uncommitted progenitor cells. 59 iScience Article analogous expression pattern appears in normal bladder, and in MIBC. 33 GATA3 is well characterized to be associated with PPARg and FOXA1 as an urothelial differentiation marker and driver of the luminal MIBC subtype. Counterintuitively, few studies have investigated GATA3 as a regulator of PPARg expression in the context of urothelial carcinoma. In our genome-wide knockout screen, GATA3 ranked first as a possible PPARG regulator, and siRNA knockdown of GATA3 resulted in decreased PPARg at the mRNA and protein level. Investigations into the regulation of PPARG by GATA3 have reported contrasting observations. A report on differentiation of pre-adipocytes showed a role of GATA3 in suppressing PPARG, while overexpression of the same transcription factor in buccal epithelial cells failed to display any effect. 61,62 Similarly, PPARG did not emerge as significantly upregulated gene by GATA3 overexpression in 5637 basal bladder cancer cells. 33 However, 5637 cells contain a CASC15-PPARg fusion, 18 which may alter the normal regulation of PPARG expression. Furthermore, these discrepancies underline the importance of the biological context for GATA3 target selection. Given the variety of tissues and cell types in which GATA3 plays a crucial role, it is not surprising that context-dependent differential expression of binding partners alters its activity. In support of this hypothesis, a study conducted on normal human urothelial cells that silenced GATA3 gene expression reported a reduction in PPARG expression, in agreement with our findings. 54 We identified and validated GATA3 as a positive regulator of PPARG in bladder cancer. Our data are supported by a recent study that performed GATA3 ChIP-Seq on the luminal bladder cancer cell line RT4 and identified GATA3 binding to enhancer regions of PPARG. 63 Consistent with GATA3 regulating PPARG by binding DNA at distal sites, identification of components of the mediator and cohesin complexes as hits in our screen suggests that chromatin looping and binding of transcription factors at distal enhancer sites are involved in the regulation of PPARG. 64 Alternatively, GATA3 may indirectly regulate PPARg by altering cell differentiation or luminal phenotypes. In agreement with this, FOXA1, a luminal differentiation driver together with PPARg and GATA3, was also identified as a top hit. Further experiments will elucidate the exact mechanism by which GATA3 drives PPARG expression.
Chromatin remodelers and components of the cohesin complex have been less well studied in the context of bladder cancer. Cohesin components including SMC1A, SMC3, RAD21, NIPBL, MAU2, and other functionally related proteins, such as MED and CTCF factors, ranked highly in our screen, suggesting a deep involvement of chromatin structuring elements in the regulation of PPARG gene expression. Here, we reported a strong inhibition of PPARG expression following loss of SMC1A or RAD21. Not all members of the cohesin complex were identified as regulators of PPARG expression, which suggests that distinct cohesin components, or their relative abundance, may confer transcriptional target specificity to the whole complex. Moreover, the observed downregulation of RAD21 protein upon SMC1A silencing might have unveiled an intrinsic regulatory mechanism of the cohesin members, which indirectly affects PPARg expression. In addition, we reported that the histone chaperone SPT6 is important in sustaining PPARG expression bladder cancer cells. This agrees with data from human mesenchymal stem cells in which downregulation of SUPT6H results in reduction of PPARG expression, which was essential in their differentiation. 65 Similarly, the cohesin complex is involved in cell differentiation, particularly during hematopoiesis 66 and thymocyte development. 67 Altogether, we identified regulators of PPARG gene expression which, despite their diverse molecular mechanisms, all have important roles in cellular differentiation as a common feature. Our data place PPARg downstream of these major differentiating factors, and further experimentation will shape the full details of the pathways involved.
Overall, we have generated a reporter system to monitor changes in PPARG gene expression in bladder cancer cells. Using this system, we performed a genome-wide CRISPR knockout screen that generated a robust dataset valuable to the study of PPARg and bladder cancer biology. We also validated four hits, spanning distinct biological processes, that support the validity and biological relevance of the reporter system and whole-genome knockout screen data presented here. Finally, these data support the utility of the reporter system to assess potential therapies targeting pathways that regulate PPARG gene expression in luminal bladder cancer.

Limitations of the study
A high level of PPARG mRNA is a common feature of luminal MIBC. 16 However, the upstream components that regulate PPARG expression in these tumors remain unclear. We therefore sought to identify putative regulators of PPARG mRNA expression by performing a genome-wide CRISPR knockout screen in a luminal bladder cancer reporter cell line. The major aim of the study was to identify potential regulators of PPARG iScience Article gene expression in bladder cancer and to generate a publicly available resource to drive and support future studies. Additional validation was used to provide evidence of the potential biological relevance of the data, but the in vivo relevance of the hits was not dissected in detail.
Although we confirmed some key hits in a second cell lines, the use of a single cell line to perform the screen constitutes a limitation of the study. Furthermore, the use of a luminal cell line with high PPARG expression likely limited our ability to identify negative regulators of PPARG expression, as many of them would not be active in our system. These limitations could be overcome in future studies by increasing the number of cell lines used for the screen including basal cell lines with low PPARG expression. Alternatively, a gene activator screen could be used in the luminal cell lines to better identify negative regulators of PPARG gene expression.
An additional limitation of our study is the limited in vivo data to support our findings. Attempting to address this, we assessed the correlation of the mRNA levels of our screen hits with PPARG in the TCGA patient cohort. However, there are a number of caveats to this approach. One of these caveats is that bulk gene expression data from tissue samples include gene expression from a heterogeneous population of cells comprised not only tumor cells but also other stromal cells such as fibroblasts and immune cells. The mechanisms of regulation of PPARG gene expression may be different in different cell types, which would therefore confound the comparison between data from a cell line and whole tissue. Another caveat to this analysis is the use of gene expression correlation to validate hits from a gene knockout. The screen described here used gene knockouts to identify putative regulators of PPARG gene expression without information about the relative expression of each of the hits. Although useful as a means to support the overall validity of our findings, there is no requirement for correlation between the level of mRNA encoding for proteins that regulate expression of a gene, and the mRNA level of that target gene. Therefore, future studies that carefully validate the putative regulators in vivo, such as through orthotopic cell line or patient-derived xenograft models, are required.
Overall, despite the limitations of the study, the data presented here fulfill the major aim of our study, and provide a valuable resource for the community that can serve to support future work investigating PPARg biology in bladder cancer.

STAR+METHODS
Detailed methods are provided in the online version of this paper and include the following:

Lentiviral packaging
For general lentivirus production, HEK293FT cells were seeded at 4 3 10 6 cell per 10cm plate, 24 hours before transient transfection with psPAX2 (4.5 mg), pMD2.G (1.5 mg) and the desired lentiviral plasmid (6 mg). 48 hours post transfection, lentivirus-containing supernatant was spun and filtered through a 0.45 mm filter to remove cellular debris.
For packaging of the pooled CRISPR library used in the whole genome knockout screen, the protocol published by Joung et al. was followed. 68 Briefly, 1.8 3 10 7 HEK293FT cells were plated in T-225 tissue culture flasks (Corning) 24 hours prior transfection. For each flask, packaging plasmids psPAX2 (23.4 mg), pMD2.G (15.3 mg) and pooled library (30.6 mg) were combined and transfected using Lipofectamine 3000 reagent as per manufacturer's protocol. Medium was changed at 6 hours post transfection to avoid reagent toxicity and 2mM of caffeine 99% (Sigma) was added at 24 hours. At 48 hours post-transfection, virus-containing supernatant was then collected as above.

Transduction and cell culture and isolation for the CRISPR screen
Transduction was performed by centrifugation (a process known as spinfection 78 and viral multiplicity of infection (MOI) was calculated as follows. 3 3 10 6 cells per well were added into a 12 well plate with 1mL of MEM medium supplemented with 8 mg/ml polybrene. Different titrated amounts of viral supernatant were added to each well along with a no-transduction control. Spinfection was carried out by spinning the plate at 1,000 g for 2 hours at 37 C followed by overnight incubation at 37 C. Cells were then collected and each condition split into 4 wells of a 96-well clear-bottom black tissue culture plate (n = 6) (Corning